This is an experimental instruction-edit LoRA for Flux.2 Klein intended to improve the visual quality of images that were produced by the Qwen-Image VAE decoder (i.e. any image made with any version of Qwen-Image or Qwen-Image-Edit using the default VAE).Qwen-Image's VAE produces noticeably washed out details and a checkerboard noise pattern. By leveraging the Flux.2 VAE, which is able to produce higher-fidelity images, these issues can be fixed to some extent.I would recommend starting with cfg at 1.0 as a baseline. Pushing cfg past 3.0 seems to result in an increasing amount of artifacts.❗ This is not intended to be a general purpose detailer. It expects input images with typical Qwen-Image VAE artifacts and may produce poor results on other images. This includes images generated with Qwen-Image models using e.g. the Wan2.1 upscale2x VAE instead of the default VAE. ❗<h3 id="training">Training</h3>The LoRA was trained on a selection of 1024x1024 photos before and after being encoded and decoded with the Qwen-Image VAE with no changes made to the latent as illustrated in this workflow:<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/a7eefe0a-2fd6-40a6-98de-e27568e87362/original=true/a7eefe0a-2fd6-40a6-98de-e27568e87362.jpeg" />Version 1.0 was trained on a dataset of 23 image pairs, mostly focused on skin, faces/hair, and vegetation. There is likely room for improvement with a more diverse dataset, but it's hard to say how much.

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